Development and interpretability of a machine learning model for postoperative hypothermia prediction in placenta previa Cesarean section: A retrospective cohort study.

ObjectiveTo develop and validate an explainable machine learning prediction model for perioperative hypothermia in patients undergoing cesarean delivery for placenta previa, and to identify key predictive factors to support clinical individualized temperature management and nursing decision-making.MethodsThis study is a retrospective secondary analysis conducted using BioStudies public databases. Ten machine learning algorithms, including logistic regression, decision tree, and random forest, were used to build hypothermia prediction models. Accuracy, precision, recall, F1-score, and AUC were selected as evaluation metrics to screen for the optimal model, and LIME was applied to generate individualized explanations for single-sample prediction outputs. A total of 167 real-world patients served as the independent test cohort, while 1000 synthetic data cases generated by the Synthetic Data Vault (SDV) algorithm served as the training cohort.ResultsPerformance comparisons across models showed the random forest model reached an AUC of 0.781 in the training set, but only 0.638 in the test set, a sign of severe overfitting. Gradient boosting models had a universally low test set recall of below 0.25, carrying a high risk of missed diagnosis. The logistic regression (LR) model exhibited the optimal overall performance: it attained the highest test-cohort accuracy among all evaluated models, with an AUC of 0.705, F1-score of 0.430, and recall of 0.327. Feature importance analysis found intraoperative blood loss to be the strongest predictor of hypothermia, followed by body weight as the second core contributing factor. LIME can intuitively visualize feature contributions for individual sample predictions. These outputs enable nurses to develop tailored warming intervention regimens.ConclusionThe logistic regression prediction model developed in this study may serve as a clinically useful tool with adequate generalization capacity and clinical interpretability for identifying hypothermia risk in patients undergoing cesarean delivery for placenta previa.